id,node_id,number,title,user,state,locked,assignee,milestone,comments,created_at,updated_at,closed_at,author_association,active_lock_reason,draft,pull_request,body,reactions,performed_via_github_app,state_reason,repo,type 1593589979,I_kwDOAMm_X85e_EDb,7545,DataArrray.resample() appears to ignore skipna parameter while taking annual mean,76110149,open,0,,,3,2023-02-21T14:33:46Z,2023-02-21T17:04:46Z,,CONTRIBUTOR,,,,"### What happened? The skipna parameter appears to have no effect while annually resampling a monthly DataArray. I'm not sure if this is related to [#6772](https://github.com/pydata/xarray/issues/6772) ### What did you expect to happen? I expected `da_yrA` to be NaN since I am not skipping NaN values. ### Minimal Complete Verifiable Example ```Python import numpy as np import pandas as pd import xarray as xr data = np.arange(12) month_index = pd.date_range(""2022-01"",""2022-12-31"", freq=""M"") df = pd.DataFrame(index=month_index, data=data) df.index.name = ""time"" df.columns.name = ""value"" df.loc[""2022-01"":""2022-02""] = np.nan da = xr.DataArray(df) # these unexpectedly produce the same result da_yrA = (da).resample(time=""A-Dec"", skipna=False).mean() da_yrB = (da).resample(time=""A-Dec"", skipna=True).mean() print(da_yrA) print(da_yrB) ``` ### MVCE confirmation - [X] Minimal example — the example is as focused as reasonably possible to demonstrate the underlying issue in xarray. - [X] Complete example — the example is self-contained, including all data and the text of any traceback. - [X] Verifiable example — the example copy & pastes into an IPython prompt or [Binder notebook](https://mybinder.org/v2/gh/pydata/xarray/main?urlpath=lab/tree/doc/examples/blank_template.ipynb), returning the result. - [X] New issue — a search of GitHub Issues suggests this is not a duplicate. ### Relevant log output _No response_ ### Anything else we need to know? _No response_ ### Environment
``` scipy: 1.10.0 netCDF4: 1.6.2 pydap: None h5netcdf: None h5py: None Nio: None zarr: None cftime: 1.6.2 nc_time_axis: 1.4.1 PseudoNetCDF: None rasterio: None cfgrib: None iris: None bottleneck: None dask: 2023.2.0 distributed: 2023.2.0 matplotlib: 3.7.0 cartopy: 0.21.1 seaborn: None numbagg: None fsspec: 2023.1.0 cupy: None pint: None sparse: 0.13.0 flox: None numpy_groupies: None setuptools: 67.3.2 pip: 23.0.1 conda: None pytest: None mypy: None IPython: 8.10.0 sphinx: None ```
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